Spatial Data Visualization

ESM 244

# Read in the CA DFW Oil Spill Incident Tracking data
ca_oil <- read_sf(here("ds394"), layer = "ds394") %>% 
  clean_names()

# Check the projection:
st_crs(ca_oil) # EPSG = 3310

# Read in the CA county data (TIGER shapefile):
ca_counties <- read_sf(here("ca_counties"), layer = "CA_Counties_TIGER2016") %>% 
  clean_names() %>% 
  select(name)

# Check the projection
st_crs(ca_counties) # EPSG = 3857

# Transform ca_counties into ca_oil
ca_counties <- st_transform(ca_counties, st_crs(ca_oil))

Interactive Map

• Below is an interactive map showing the locations of oil spills in 2008 in and off the coast of California. By clicking on a gold dot you can uncover more information about each oil spill incident.

# interactive mode
tmap_mode("view")

# tmap with CA and oil spill locations
tm_basemap("Esri.WorldStreetMap") +
tm_shape(ca_counties) +
  tm_fill(col = "navyblue", alpha = 0.75) +
  tm_borders(col = "black") +
tm_shape(ca_oil) +
  tm_dots(col = "gold",
          alpha = 0.8) 

Static Chloropleth

• Below is a map of California that shows the number of inland oil spill incidents per county in 2008. The darkest colored county is Los Angeles county where 340 oil spill incidents were reported in 2008.

# Filter for inland only
ca_oil_static <- ca_oil %>% 
  filter(inlandmari == "Inland") 

# Join counties with oil dataset
chlor_data <- ca_counties %>%
  st_join(ca_oil_static) %>% 
  count(localecoun)

# Chloropleth Plot
ggplot(data = chlor_data) +
  geom_sf(aes(fill = n), color = "white", size = 0.1) +
  scale_fill_gradient(low = "gold", high = "navyblue") +
  theme_void() +
  labs(caption = "Chloropleth of counts of inland oil spills in 2008 in California",
       fill = "Number of oil spills\nwithin county") +
  theme(legend.title = element_text(face = "bold", size = 12, color = "black"),
        legend.text = element_text(face = "bold", size = 11, color = "black"),
        legend.position = "right",
        plot.caption = element_text(face = "bold", size = 11, color = "black"),
        plot.background = element_rect(fill = "white",
                                       color = "white"))